“July Effect” in Elective Spine Surgery
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cohort. OBJECTIVE: To evaluate for the presence and magnitude of the "July effect" within elective spine surgery. SUMMARY OF BACKGROUND DATA: The July effect is the hypothetical increase in morbidity and mortality thought to be associated with the influx of new (or newly promoted) trainees during the first portion of the academic year. Studies evaluating for the presence and magnitude of the July effect have demonstrated conflicting results. METHODS: We accessed the American College of Surgeons National Surgical Quality Improvement Program database from 2005-2010. Statistical analyses were conducted using bivariate and multivariate logistic regression. RESULTS: A total of 14,986 cases met inclusion criteria and constitute the study population. Of these, 26.5% occurred in the first academic quarter and 25.3% had resident involvement. The rate of serious adverse events was 1.9 times higher and the rate of any adverse events was 1.6 times higher among cases with resident involvement than among those without (P < 0.001 for both). Among cases without resident involvement, the rates of serious adverse events and any adverse events did not differ by academic quarter. Similarly, among cases with resident involvement, the rates of serious adverse events and any adverse events did not differ by academic quarter. CONCLUSION: We could not demonstrate that the training of new (or newly promoted) residents is associated with an increase in the adverse events of spine surgery. Safeguards that have been put in place to ensure patient safety during this training period seem to be effective. Although adverse events were more common among cases with resident involvement than among cases without resident involvement, our data suggest that this association is more likely a product of the riskier population of cases in which residents participate than of the resident involvement itself.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".